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Predicting occult lymph node metastasis in esophageal squamous cell carcinoma using deep learning and radiomics models based on CT imaging

Predicting occult lymph node metastasis in esophageal squamous cell carcinoma using deep learning and radiomics models based on CT imaging

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500111371
Enrollment
Unknown
Registered
2025-10-30
Start date
2025-10-30
Completion date
Unknown
Last updated
2025-11-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Esophageal squamous cancer

Interventions

Training group:None
Internal test group:None
External test group:None

Sponsors

The First Affiliated Hospital with Nanjing Medical University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Clinical examination, esophagogastroduodenoscopy, CT/MRI confirmed that the lymph nodes are cN0 stage; 2. Postoperative pathology confirmed esophageal squamous cell carcinoma and lymphadenectomy was performed; 3. Imaging data of the patient within two weeks before the surgery.

Exclusion criteria

Exclusion criteria: 1. The patient has received other treatments before surgery (radiotherapy, chemotherapy, neoadjuvant therapy, etc.); 2. There is evidence of distant metastasis or multiple tumors; 3. Poor image quality or incomplete clinical data.

Design outcomes

Primary

MeasureTime frame
High-risk of occult lymph node metastasis;Low-risk of occult lymph node metastasis;—

Countries

China

Contacts

Public ContactLi Qiong

The First Affiliated Hospital with Nanjing Medical University

njmu_lq@163.com+86 152 3550 0083

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026